benchmarking-gnns
Repository for benchmarking graph neural networks (by graphdeeplearning)
graphein
Protein Graph Library (by a-r-j)
| benchmarking-gnns | graphein | |
|---|---|---|
| 1 | 2 | |
| 2,433 | 986 | |
| 1.5% | - | |
| 0.0 | 7.8 | |
| 11 months ago | 12 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
benchmarking-gnns
Posts with mentions or reviews of benchmarking-gnns.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-27.
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[D] Laplacian positional encodings
Code for https://arxiv.org/abs/2003.00982 found: https://github.com/graphdeeplearning/benchmarking-gnns
graphein
Posts with mentions or reviews of graphein.
We have used some of these posts to build our list of alternatives
and similar projects.
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Meet Graphein: a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks
Github: https://github.com/a-r-j/graphein
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[Discussion] which NN architecture is best suitable for analysing the structural data of biomolecules
As alluded to by u/WorldWar1Nerd, it depends on the format and structure of your data. However, based on what you have already said about your dataset, a graph neural network (GNN) may be a suitable choice, depending on the task. I recommend looking into a wonderful ML library for proteins called Graphein (https://github.com/a-r-j/graphein) to get started, however, do not be afraid if you find that you need to customize these methods to your specific problem.
What are some alternatives?
When comparing benchmarking-gnns and graphein you can also consider the following projects:
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
AllenSDK - code for reading and processing Allen Institute for Brain Science data
fastai - The fastai deep learning library
netsci-labs - (In progress) Network science laboratories. Covers graph theory, random graphs and ML on graphs
ktrain - ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
geometric-gnn-dojo - Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.